
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "74"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 25 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 25 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break


for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2460008	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.876225	0.079943	-0.019112	-0.570351	-0.769493	2.183490	-0.688498	0.503253	0.6672	0.6848	0.3214	nan	nan
2460007	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.487656	0.445009	-0.223207	-0.268683	-0.683698	1.026788	-1.443063	3.711735	0.6351	0.6470	0.3438	nan	nan
2459999	RF_maintenance	0.00%	89.06%	85.63%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.1319	0.1616	0.0517	nan	nan
2459998	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	-0.879261	-0.252030	0.306471	-0.870850	-0.601645	1.298811	-0.561626	4.470152	0.6268	0.6391	0.3739	nan	nan
2459997	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	-0.654576	0.166427	0.101643	-0.235395	-0.553868	1.305885	-0.629878	4.412259	0.6420	0.6563	0.3775	nan	nan
2459996	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.083364	0.285327	-0.123543	-0.335109	-0.537313	1.636108	-1.065580	0.611337	0.6466	0.6555	0.3917	nan	nan
2459995	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.888607	0.129110	-0.109051	-0.627075	-0.400969	1.836452	-1.072476	1.276875	0.6430	0.6550	0.3746	nan	nan
2459994	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.749562	-0.007685	0.428777	-0.865025	-0.572817	0.928103	-1.387774	1.461166	0.6366	0.6475	0.3742	nan	nan
2459993	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.061593	0.414240	0.135672	-0.443775	0.044868	2.168280	-0.997641	1.900106	0.6322	0.6561	0.3827	nan	nan
2459991	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.345299	0.013029	0.013713	-0.344819	-0.174036	1.446803	-1.024383	2.226698	0.6413	0.6442	0.3776	nan	nan
2459990	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.980030	-0.053736	0.008115	-0.414566	0.125767	1.098833	-0.486294	3.381127	0.6445	0.6492	0.3766	nan	nan
2459989	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.929429	-0.135067	0.225930	-0.136830	-0.019081	1.122034	-0.603430	2.631396	0.6401	0.6476	0.3798	nan	nan
2459988	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.096476	-0.318882	0.005630	-0.625800	-0.033105	1.697741	-0.874767	3.028135	0.6340	0.6415	0.3730	nan	nan
2459987	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	-0.478053	0.119238	-0.034688	-0.399727	-0.206639	1.309626	0.219750	4.533075	0.6441	0.6518	0.3674	nan	nan
2459986	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.675309	0.181181	-0.015865	-0.568146	-0.162129	1.618217	-0.426019	1.787351	0.6516	0.6648	0.3242	nan	nan
2459985	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.571204	0.071660	-0.123291	-0.412632	-0.685193	1.325732	-1.124588	3.068185	0.6319	0.6380	0.3704	nan	nan
2459984	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.265699	0.447142	-0.005243	-0.514688	-0.431358	1.891020	0.739570	2.237432	0.6536	0.6601	0.3519	nan	nan
2459983	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.796750	0.060741	-0.067891	-0.525677	-0.172419	1.425663	-0.167067	2.407142	0.6624	0.6840	0.3128	nan	nan
2459982	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.181640	-0.703788	-0.483359	-0.139204	0.310467	1.240890	-0.940039	0.221580	0.7144	0.7203	0.2801	nan	nan
2459981	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.212941	-0.115192	0.036705	-0.665046	0.012510	1.031769	-0.838602	3.394905	0.6408	0.6489	0.3694	nan	nan
2459980	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-0.982933	0.046763	-0.257635	-0.455464	-0.076487	0.260159	-0.914945	0.018572	0.6852	0.6966	0.3007	nan	nan
2459979	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.346506	-0.050870	-0.157316	-0.496528	0.232185	1.157927	-0.608923	2.854996	0.6310	0.6426	0.3696	nan	nan
2459978	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	-1.341501	-0.005617	-0.023707	-0.621417	0.183805	1.616103	-0.034888	5.597204	0.6295	0.6404	0.3753	nan	nan
2459977	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	-0.992938	0.259956	-0.148609	-0.501270	0.951021	1.727730	-0.132002	4.341197	0.6015	0.6143	0.3419	nan	nan
2459976	RF_maintenance	0.00%	0.00%	0.00%	0.00%	-	-	-1.245846	0.045627	-0.152972	-0.589195	0.083309	1.168172	-0.633855	2.969696	0.6454	0.6539	0.3667	nan	nan

Antenna Season Report Notebook¶

Antenna 74 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 74, Node N05:

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 74: 2460008

Antenna 74: 2460007

Antenna 74: 2459999

Antenna 74: 2459998

Antenna 74: 2459997

Antenna 74: 2459996

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Antenna 74: 2459979

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Antenna 74: 2459976

Antenna Season Report Notebook¶

Antenna 74 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 74, Node N05:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 74: 2460008

Antenna 74: 2460007

Antenna 74: 2459999

Antenna 74: 2459998

Antenna 74: 2459997

Antenna 74: 2459996

Antenna 74: 2459995

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Antenna 74: 2459993

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Antenna 74: 2459977

Antenna 74: 2459976

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶